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Machine Learning with Python

Machine Learning with Python

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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

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📈 Análisis del canal de Telegram Machine Learning with Python

El canal Machine Learning with Python (@codeprogrammer) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 67 813 suscriptores, ocupando la posición 2 417 en la categoría Educación y el puesto 5 033 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 813 suscriptores.

Según los últimos datos del 11 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 56, y en las últimas 24 horas de 0, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.96%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.43% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 2 683 visualizaciones. En el primer día suele acumular 1 650 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 6.
  • Intereses temáticos: El contenido se centra en temas clave como insidead, learning, degree, evaluation, algorithm.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 12 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.

67 813
Suscriptores
Sin datos24 horas
-127 días
+5630 días
Archivo de publicaciones
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🖥 Large Language Model Course The popular free LLM course has just been updated. This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base. The course is divided into 3 parts: 1️⃣ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks. 2️⃣ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks. 3️⃣ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them. ⭐️ 41.4k stars on Github 📌 https://github.com/mlabonne/llm-course #llm #course #opensource #ml https://t.me/CodeProgrammer 👑

To generate stunning maps from OpenStreetMap data in #Python, use Prettymaps. It enables map creation with minimal code and o
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Machine Learning concepts - Intermediate Level. Convolutional Neural Network (CNN) Layers: Convolution vs Pooling vs Fully Co
Machine Learning concepts - Intermediate Level. Convolutional Neural Network (CNN) Layers: Convolution vs Pooling vs Fully Connected #MachineLearning #NeuralNetwork #DeepLearning #ArtificialIntelligence #AI #Algorithms https://t.me/CodeProgrammer 🐍

Machine Learning Algorithm basics! Support Vector Machines (SVM) vs k-Nearest Neighbors (k-NN) #MachineLearning #algorithms #
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